1. Field of the Invention
The present invention relates to multi-dimensional data processing, and more particularly, to the enhancement of image data.
2. Background Information
Imaging systems play a varied and important role in many different applications. For example, medical imaging applications, such as endoscopy, fluoroscopy, X-ray, arthroscopy, and microsurgery applications are helping to save lives and improve health. Industrial applications, such as parts inspection systems that can detect microscopic errors on an assembly line, are leading to increased yields and efficiencies. A wide variety of military and law enforcement applications are taking advantage of imaging technology for target acquisition, surveillance, night vision, etc. Even consumer applications are taking advantage of advanced video imaging technology to produce heightened entertainment experience, such as the improved picture quality provided by High Definition Television (HDTV).
While there have been many advancements in video imaging technology, conventional video imaging systems can still suffer from deficiencies that impact the quality and usefulness of the video imagery produced. For example, video images generated with uncontrolled illumination often contain important, but subtle, low-contrast details that can be obscured from the viewer's perception by large dynamic range variations in the image. Any loss, or difficulty, in perceiving such low-contrast details can be detrimental in situations that require rapid responses to, or quick decisions based on, the images being presented.
A number of techniques have been applied to enhance video imagery. These techniques include image filtering applied in real-time. Conventional real-time filtering techniques can today be implemented as digital convolution over kernels comprising a relatively small number of image pixels, e.g., 3×3 pixel kernels, or 7×7 pixel kernels. These techniques can use high-pass filtering to emphasize details that are small relative to the size of the kernel being used. The improvement that can be achieved using such small kernels, however, is often limited. Studies have shown that significantly larger kernels are far more effective at achieving meaningful video enhancement. Unfortunately, the processing overhead required to perform large kernel convolution, in real-time, using conventional techniques, is prohibitive at the present state of digital signal processing technology.